Machine Learning
Fairness is Not Flat: Geometric Phase Transitions Against Shortcut Learning
Nicolas Rodriguez-Alvarez
Deep Neural Networks are highly susceptible to shortcut learning, frequently memorizing low-dimensional spurious correlations instead of underlying causal mechanisms. This phenomenon not only degrades out-of-distribution robustness but also induces severe demographic biases in sensitive applications. In this paper, we propose a
geometric a priori methodology to mitigate shortcut learning. By deploying a zero-hidden-layer ($N=1$) Topological Auditor, we mathematically isolate features that monopolize the gradient without human intervention. We empirically demonstrate a Capacity Phase Transition: once linear shortcuts are pruned, networks are forced to utilize higher
geometric capacity ($N \geq 16$) to curve the decision boundary and learn ethical representations. Our approach outperforms L1 Regularization – which collapses into demographic bias – and operates at a fraction of the computational cost of post-hoc methods like Just Train Twice (JTT), successfully reducing counterfactual gender vulnerability from 21.18% to 7.66%.
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Machine Learning · Cross-listed from cs.CL
Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning
Jieying Xue, Phuong Minh Nguyen, Ha Thanh Nguyen et al. (+2 more)
This work aims to improve the generalization of logic-based legal
reasoning systems by integrating recent advances in NLP with legal-domain adaptive few-shot learning techniques using LLMs. Existing logic-based legal
reasoning pipelines typically rely on fine-tuned models to map natural-
language legal cases into logical formulas before forwarding them to a symbolic reasoner. However, such approaches are heavily constrained by the scarcity of high-quality annotated training data. To address this limitation, we propose a novel LLM-based legal
reasoning framework that enables effective in-context learning through
retrieval-augmented generation. Specifically, we introduce Legal2LogicICL, a few-shot
retrieval framework that balances diversity and similarity of exemplars at both the latent semantic representation level and the legal text structure level. In addition, our method explicitly accounts for legal structure by mitigating entity-induced
retrieval bias in legal texts, where lengthy and highly specific entity mentions often dominate semantic representations and obscure legally meaningful
reasoning patterns. Our Legal2LogicICL constructs informative and robust few-shot demonstrations, leading to accurate and stable logical rule generation without requiring additional training. In addition, we construct a new
dataset, named Legal2Proleg, which is annotated with
alignments between legal cases and PROLEG logical formulas to support the evaluation of legal semantic parsing. Experimental results on both open-source and proprietary LLMs demonstrate that our approach significantly improves accuracy, stability, and generalization in transforming natural-
language legal case descriptions into logical representations, highlighting its effectiveness for interpretable and reliable legal
reasoning. Our code is available at https://github.com/yingjie7/Legal2LogicICL.
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All authors Jieying Xue, Phuong Minh Nguyen, Ha Thanh Nguyen, May Myo Zin, Ken Satoh
Machine Learning · Cross-listed from cs.CL
Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind
Hanqi Xiao, Vaidehi Patil, Zaid Khan et al. (+3 more)
As large
language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and use a theory-of-mind, or ToM) becomes increasingly critical for safe interaction with potentially
adversarial partners. We propose a novel
privacy-themed ToM challenge, ToM for Steering Beliefs (ToM-SB), in which a defender must act as a Double
Agent to steer the beliefs of an attacker with partial prior knowledge within a shared universe. To succeed on ToM-SB, the defender must engage with and form a ToM of the attacker, with a goal of fooling the attacker into believing they have succeeded in extracting sensitive information. We find that strong frontier models like Gemini3-Pro and GPT-5.4 struggle on ToM-SB, often failing to fool attackers in hard scenarios with partial attacker prior knowledge, even when prompted to reason about the attacker's beliefs (ToM prompting). To close this gap, we train models on ToM-SB to act as AI Double
Agents using reinforcement learning, testing both fooling and ToM rewards. Notably, we find a bidirectionally emergent relationship between ToM and attacker-fooling: rewarding fooling success alone improves ToM, and rewarding ToM alone improves fooling. Across four attackers with different strengths, six defender methods, and both in-distribution and out-of-distribution (OOD) evaluation, we find that gains in ToM and attacker-fooling are well-correlated, highlighting belief modeling as a key driver of success on ToM-SB. AI Double
Agents that combine both ToM and fooling rewards yield the strongest fooling and ToM performance, outperforming Gemini3-Pro and GPT-5.4 with ToM prompting on hard scenarios. We also show that ToM-SB and AI Double
Agents can be extended to stronger attackers, demonstrating generalization to OOD settings and the upgradability of our task.
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All authors Hanqi Xiao, Vaidehi Patil, Zaid Khan, Hyunji Lee, Elias Stengel-Eskin, Mohit Bansal
Machine Learning
Towards Autonomous Mechanistic Reasoning in Virtual Cells
Yunhui Jang, Lu Zhu, Jake Fawkes et al. (+3 more)
Large
language models (LLMs) have recently gained significant
attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scientific domains such as biology remains limited, primarily due to the lack of factually grounded and actionable explanations. To address this, we introduce a structured explanation formalism for virtual
cells that represents biological
reasoning as mechanistic action graphs, enabling systematic verification and falsification. Building upon this, we propose VCR-
Agent, a multi-
agent framework that integrates biologically grounded knowledge
retrieval with a verifier-based filtering approach to generate and validate mechanistic
reasoning autonomously. Using this framework, we release VC-TRACES
dataset, which consists of verified mechanistic explanations derived from the Tahoe-100M atlas. Empirically, we demonstrate that training with these explanations improves factual precision and provides a more effective supervision signal for downstream gene expression prediction. These results underscore the importance of reliable mechanistic
reasoning for virtual
cells, achieved through the synergy of multi-
agent and rigorous verification.
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All authors Yunhui Jang, Lu Zhu, Jake Fawkes, Alisandra Kaye Denton, Dominique Beaini, Emmanuel Noutahi
Machine Learning · Cross-listed from cs.CR
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López et al. (+7 more)
Fully homomorphic encryption (FHE) has recently attracted significant
attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated computing, FHE presents a promising opportunity for progress, with applications ranging from machine learning to information security. We target the most computationally intensive operation in deep neural networks from a hardware perspective,
matrix multiplication (matmul), and adapt it for execution on AMD GPUs. We propose a new optimized method that improves the runtime and complexity of ciphertext matmul by using FIDESlib, a recent open-source FHE library designed specifically for GPUs. By exploiting sparsity in both operands, our sparse matmul implementation outperforms its CPU counterpart by up to $3.0\times$ and reduces the time complexity from cubic to semi-linear, demonstrating an improvement over existing FHE matmul implementations.
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All authors Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López, Kaustubh Shivdikar, Ardhi W. B. Yudha, Ferhat Yaman, David Kaeli, José L. Abellán, Ian Colbert, José Cano
Machine Learning
Inter-Layer Hessian Analysis of Neural Networks with DAG Architectures
Maxim Bolshim and Alexander Kugaevskikh
Automatic differentiation computes loss Hessians, while interpreting inter-layer interactions requires an explicit choice of graph-indexed blocks. This paper presents an analytical formalism that decomposes the full Hessian into blocks indexed by the DAG of an architecture. The standard decomposition $H = H^{GN} + H^T$ separates the Gauss-Newton component (positive semidefinite for a convex output loss) from the tensor component (residual
curvature). If every node map is locally affine in its parent activations, the tensor component of the input Hessian vanishes on each smooth
cell ($H^{T}_{v,w}=0$, $H^f_{v,w}=H^{GN}_{v,w}$); ReLU activations alone do not suffice. The full parametric Hessian contains residual terms that do not reduce to the GGN. Building on this decomposition, we use diagnostic metrics (inter-layer resonance $\mathcal{R}$,
geometric coupling $\mathcal{C}$, stable rank $\mathcal{D}$, GN-Gap) with
stochastic HVP-based estimates whose cost depends on graph evaluation and the number of probes. The theoretical analysis gives conditional bounds on
curvature decay and residual-chain transport; empirical examples span fully connected MLPs (Exp. 1-5) and convolutional architectures (ResNet-18, ${\sim}11$M parameters, Exp. 6). When the architecture reduces to a single node, all definitions collapse to the standard Hessian $\nabla^2_\theta\mathcal{L}(\theta)\in\mathbb{R}^{p\times p}$. Archival correction. The original general novelty claims are no longer maintained: general DAG-Hessian calculus has prior precedents. This is a corrected archival version.
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Machine Learning · Cross-listed from cs.AI
RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time
Haozhe Wang, Cong Wei, Weiming Ren et al. (+3 more)
Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the
reasoning that underlies preference. We show that teaching reward models to produce explicit, multi-dimensional critiques before scoring transforms them from passive evaluators into active optimization tools, improving generators in two complementary ways: at training time, structured rationales provide interpretable, fine-grained rewards for reinforcement learning; at test time, a Generate-Critique-Refine loop turns critiques into targeted
prompt revisions that improve outputs without any parameter updates. To train such a reward model without costly rationale annotations, we introduce Preference-Anchored Rationalization (PARROT), a principled framework that recovers high-quality rationales from readily available preference data through anchored generation, consistency filtering, and
distillation. The resulting model, RationalRewards (8B), achieves state-of-the-art preference prediction among open-source reward models, competitive with Gemini-2.5-Pro, while using 10-20x less training data than comparable baselines. As an RL reward, it consistently improves text-to-image and image-editing generators beyond scalar alternatives. Most strikingly, its test-time critique-and-refine loop matches or exceeds RL-based fine-tuning on several
benchmarks, suggesting that structured
reasoning can unlock latent capabilities in existing generators that suboptimal
prompts fail to elicit.
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All authors Haozhe Wang, Cong Wei, Weiming Ren, Jiaming Liu, Fangzhen Lin, Wenhu Chen
Machine Learning
SCNO: Spiking Compositional Neural Operator – Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
Samrendra Roy, Souvik Chakraborty, Rizwan-uddin et al. (+1 more)
Neural operators have emerged as powerful surrogates for partial differential equation (PDE) solvers, yet they are typically trained as monolithic models for individual PDEs, require energy-intensive GPU hardware, and must be retrained from scratch when new physics emerge. We introduce the Spiking Compositional Neural Operator (SCNO), a modular architecture combining spiking and conventional components that addresses all three limitations. SCNO maintains a library of small spiking neural operator blocks, each trained on a single elementary differential operator (convection,
diffusion, reaction), and composes them through a lightweight input-conditioned aggregator to solve coupled PDEs not seen during block training. A small correction network learns cross-coupling residuals while keeping all blocks and the aggregator frozen, preserving zero-forgetting modular expansion by construction. We evaluate SCNO on eight PDE families including five coupled systems and a
nuclear-relevant 1-group
neutron diffusion equation. SCNO with correction achieves the lowest relative $L^2$ error on four of five coupled PDEs, outperforming both a monolithic spiking DeepONet (by up to 62%, mean over 3 seeds) and a standard ANN DeepONet (by up to 65%), while requiring only 95K trainable parameters versus 462K for the monolithic baseline. To our knowledge, this is the first compositional spiking neural operator and the first proof-of-concept for modular neuromorphic PDE solving with built-in forgetting-free expansion.
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All authors Samrendra Roy, Souvik Chakraborty, Rizwan-uddin, Syed Bahauddin Alam
Machine Learning · Cross-listed from cs.AR
CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead
Jinpeng Ye, Chongxi Wang, Wenqing Li et al. (+11 more)
Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often incur substantial hardware and software design overhead. Tight coupling with the CPU pipeline complicates integration across diverse CPUs, while fine-grained synchronous instructions hinder the development of high-performance kernels. This paper proposes a unified and configurable CPU
matrix extension architecture. By decoupling
matrix units from the CPU pipeline, the design enables low-overhead integration while maintaining close coordination with existing compute and
memory resources. The configurable
matrix unit supports mixed-precision operations and adapts to diverse compute demands and
memory bandwidth constraints. An asynchronous
matrix multiplication abstraction with flexible granularity conceals hardware details, simplifies
matrix-vector overlap, and supports a unified software stack. The architecture is integrated into four open-source CPU RTL platforms and evaluated on representative AI models.
Matrix unit utilization under GEMM workloads exceeds 90% across all platforms. When configured with compute throughput and
memory bandwidth comparable to Intel AMX, our design achieves speedups of 1.57x, 1.57x, and 2.31x on ResNet, BERT, and Llama3, with over 30% of the gains attributed to overlapped
matrix-vector execution. A 4 TOPS@2GHz
matrix unit occupies only 0.53 mm\textsuperscript{2} in 14nm CMOS. These results demonstrate strong cross-platform adaptability and effective hardware-software co-optimization, offering a practical
matrix extension for the open-source community.
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All authors Jinpeng Ye, Chongxi Wang, Wenqing Li, Bin Yuan, Shiyi Wang, Fenglu Zhang, Junyu Yue, Jianan Xie, Yunhao Ye, Haoyu Deng, Yingkun Zhou, Xin Cheng, Fuxin Zhang, Jian Wang
Machine Learning
Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
Patrick Lutz, Themistoklis Haris, Arjun Chandra et al. (+2 more)
Transformers can perform in-context classification from a few labeled examples, yet the inference-time
algorithm remains opaque. We study multi-class linear classification in the hard no-margin regime and make the computation identifiable by enforcing feature- and label-permutation equivariance at every layer. This enables interpretability while maintaining functional equivalence and yields highly structured weights. From these models we extract an explicit depth-indexed recursion: an end-to-end identified, emergent update rule inside a softmax
transformer, to our knowledge the first of its kind.
Attention matrices formed from mixed feature-label Gram structure drive coupled updates of training points, labels, and the test probe. The resulting dynamics implement a geometry-driven algorithmic motif, which can provably amplify class separation and yields robust expected class
alignment.
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All authors Patrick Lutz, Themistoklis Haris, Arjun Chandra, Aditya Gangrade, Venkatesh Saligrama
Machine Learning · Cross-listed from cs.CL
Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
Jiashu Yao, Heyan Huang, Zeming Liu et al. (+1 more)
To overcome the sparse reward challenge in reinforcement learning (RL) for
agents based on large
language models (LLMs), we propose Mutual Information Self-Evaluation (MISE), an RL paradigm that utilizes hindsight generative self-evaluation as dense reward signals while simultaneously calibrating them against the environmental feedbacks. Empirically, MISE enables an
agent to learn autonomously from dense internal rewards supplementing sparse extrinsic signals. Theoretically, our work provides the first formal foundation for the paradigm of generative self-rewarding. We prove that utilizing hindsight self-evaluation rewards is equivalent to minimizing an objective that combines mutual information with a KL divergence term between the policy and a proxy reward policy. This theoretical insight then informs and justifies our calibration step, which actively aligns these rewards with the optimal policy. Extensive experiments show that MISE outperforms strong baselines, enabling open-source LLMs about 7B parameters to achieve performance comparable to GPT-4o on validation without expert supervision.
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All authors Jiashu Yao, Heyan Huang, Zeming Liu, Yuhang Guo
Machine Learning · Cross-listed from math.OC
Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements
Xiang Meng, Andrés Gómez and Rahul Mazumder
We study computational aspects of a key problem in robust statistics – the penalized least trimmed squares (LTS) regression problem, a robust
estimator that mitigates the influence of outliers in data by capping residuals with large magnitudes. Although statistically attractive, penalized LTS is NP-hard, and existing mixed-integer optimization (MIO) formulations scale poorly due to weak relaxations and exponential worst-case complexity in the number of observations. We propose a new MIO formulation that embeds hyperplane arrangement logic into a perspective reformulation, explicitly enforcing structural properties of optimal solutions. We show that, if the number of features is fixed, the resulting branch-and-bound tree is of
polynomial size in the sample size. Moreover, we develop a tailored branch-and-bound
algorithm that uses first-order methods with dual bounds to solve node relaxations efficiently. Computational experiments on synthetic and real
datasets demonstrate substantial improvements over existing MIO approaches: on synthetic instances with 5000 samples and 20 features, our tailored solver reaches a 1% gap in 1 minute while competing approaches fail to do so within one hour. These gains enable exact robust regression at significantly larger sample sizes in low-dimensional settings.
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Machine Learning · Cross-listed from cs.CL
A Triadic Suffix Tokenization Scheme for Numerical Reasoning
Olga Chetverina
Standard subword tokenization methods fragment numbers inconsistently, causing large
language models (LLMs) to lose positional and decimal structure - a primary driver of errors in arithmetic and scientific
reasoning. We introduce Triadic Suffix Tokenization (TST), a deterministic scheme that partitions digits into three-digit triads and annotates each triad with an explicit magnitude marker. Critically, the scheme defines a fixed, one-to-one mapping between suffixes and orders of magnitude for the integer part (thousands, millions, billions, etc.) and a parallel system of replicated markers for fractional depth (tenths, thousandths, millionths, etc.). Unlike approaches that rely on positional inference, this method provides a consistent gradient signal, which should ensure stable convergence. Two implementation variants are proposed: (1) a vocabulary-based approach that adds at most 10,000 fixed
tokens to an existing vocabulary, covering 33 orders of magnitude ($10^{-15}$ to $10^{18}$); and (2) a suffix-marker approach that uses a small set of special
tokens to denote magnitude dynamically. Both variants preserve exact digits while making order-of-magnitude relationships transparent at the
token level. While we focus on 3-digit groups (Triadic), the framework is inherently scalable to any group size for precise vocabulary optimization. Furthermore, it allows for linear vocabulary expansion to accommodate arbitrary precision and range. TST is architecture-agnostic and can be integrated as a drop-in preprocessing step. Experimental validation is deferred to future work.
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Machine Learning · Cross-listed from quant-ph
Minimizing classical resources in variational measurement-based quantum computation for generative modeling
Arunava Majumder, Hendrik Poulsen Nautrup and Hans J. Briegel
Measurement-based
quantum computation (MBQC) is a framework for
quantum information processing in which a computational task is carried out through one-
qubit measurements on a highly entangled resource state. Due to the indeterminacy of the outcomes of a
quantum measurement, the random outcomes of these operations, if not corrected, yield a variational
quantum channel family. Traditionally, this randomness is corrected through classical processing in order to ensure deterministic
unitary computations. Recently, variational measurement-based
quantum computation (VMBQC) has been introduced to exploit this measurement-induced randomness to gain an advantage in generative modeling. A limitation of this approach is that the corresponding channel model has twice as many parameters compared to the
unitary model, scaling as $N \times D$, where $N$ is the number of logical
qubits (width) and $D$ is the depth of the VMBQC model. This can often make optimization more difficult and may lead to poorly trainable models. In this paper, we present a restricted VMBQC model that extends the
unitary setting to a channel-based one using only a single additional trainable parameter. We show, both numerically and algebraically, that this minimal extension is sufficient to generate probability distributions that cannot be learned by the corresponding
unitary model.
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Machine Learning · Cross-listed from cs.CL
Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo
Artem Gadzhiev and Andrew Kislov
Providing AI
agents with reliable long-term
memory that does not hallucinate remains an open problem. Current approaches to
memory for LLM
agents – sliding windows, summarization,
embedding-based RAG, and flat fact extraction – each reduce
token cost but introduce catastrophic information loss, semantic drift, or uncontrolled
hallucination about the user. The structural reason is architectural: every published
memory system on the LoCoMo
benchmark treats conversation as a
retrieval problem over raw or lightly summarized dialogue segments, and none reports
adversarial robustness, the ability to refuse questions about facts the user never disclosed. We present Synthius-Mem, a
brain-inspired structured persona
memory system that takes a fundamentally different approach. Instead of retrieving what was said, Synthius-Mem extracts what is known about the person: a full persona extraction pipeline decomposes conversations into six cognitive domains (biography, experiences, preferences, social circle, work, psychometrics), consolidates and deduplicates per domain, and retrieves structured facts via CategoryRAG at 21.79 ms latency. On the LoCoMo
benchmark (ACL 2024, 10 conversations, 1,813 questions), Synthius-Mem achieves 94.37% accuracy, exceeding all published systems including MemMachine (91.69%,
adversarial score is not reported) and human performance (87.9 F1). Core
memory fact accuracy reaches 98.64%.
Adversarial robustness, the
hallucination resistance metric that no competing system reports, reaches 99.55%. Synthius-Mem reduces
token consumption by ~5x compared to full-context replay while achieving higher accuracy. Synthius-Mem achieves state-of-the-art results on LoCoMo and is, to our knowledge, the only persona
memory system that both exceeds human-level performance and reports
adversarial robustness.
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Machine Learning
bacpipe: a Python package to make bioacoustic deep learning models accessible
Vincent S. Kather, Sylvain Haupert, Burooj Ghani et al. (+1 more)
1. Natural sounds have been recorded for millions of hours over the previous decades using passive acoustic monitoring. Improvements in deep learning models have vastly accelerated the analysis of large portions of this data. While new models advance the state-of-the-art, accessing them using tools to harness their full potential is not always straightforward. Here we present bacpipe, a collection of bioacoustic deep learning models and evaluation pipelines accessible through a graphical and programming interface, designed for both ecologists and computer scientists. Bacpipe is a modular software package intended as a point of convergence for bioacoustic models. 2. Bacpipe streamlines the usage of state-of-the-art models on custom audio
datasets, generating acoustic feature vectors (
embeddings) and classifier predictions. A modular design allows evaluation and benchmarking of models through interactive visualizations, clustering and probing. 3. We believe that access to new deep learning models is important. By designing bacpipe to target a wide audience, researchers will be enabled to answer new ecological and evolutionary questions in bioacoustics. 4. In conclusion, we believe accessibility to developments in deep learning to a wider audience benefits the ecological questions we are trying to answer.
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All authors Vincent S. Kather, Sylvain Haupert, Burooj Ghani, Dan Stowell
Machine Learning · Cross-listed from cs.HC
Human Centered Non Intrusive Driver State Modeling Using Personalized Physiological Signals in Real World Automated Driving
David Puertas-Ramirez, Raul Fernandez-Matellan, David Martin Gomez et al. (+1 more)
In vehicles with partial or conditional driving automation (SAE Levels 2-3), the driver remains responsible for supervising the system and responding to take-over requests. Therefore, reliable driver monitoring is essential for safe human-automation collaboration. However, most existing Driver Monitoring Systems rely on generalized models that ignore individual physiological variability. In this study, we examine the feasibility of personalized driver state modeling using non-intrusive physiological sensing during real-world automated driving. We conducted experiments in an SAE Level 2 vehicle using an Empatica E4 wearable sensor to capture
multimodal physiological signals, including electrodermal activity, heart rate, temperature, and motion data. To leverage deep learning architectures designed for images, we transformed the physiological signals into two-dimensional representations and processed them using a
multimodal architecture based on pre-trained ResNet50 feature extractors. Experiments across four drivers demonstrate substantial interindividual variability in physiological patterns related to driver awareness. Personalized models achieved an average accuracy of 92.68%, whereas generalized models trained on multiple users dropped to an accuracy of 54%, revealing substantial limitations in cross-user generalization. These results underscore the necessity of adaptive, personalized driver monitoring systems for future automated vehicles and imply that autonomous systems should adapt to each driver's unique physiological profile.
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All authors David Puertas-Ramirez, Raul Fernandez-Matellan, David Martin Gomez, Jesus G. Boticario
Machine Learning
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach
Haolin Li, Shuyang Jiang, Ruipeng Zhang et al. (+3 more)
While large
language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality
reasoning data. To address this issue, existing approaches typically distill chain-of-thought
reasoning traces from large proprietary models via supervised fine-tuning, then conduct reinforcement learning (RL). These methods exhibit limited improvement on underrepresented domains like rare diseases while incurring substantial costs from generating complex
reasoning chains. To efficiently enhance medical
reasoning, we propose MedSSR, a Medical Knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework. Our framework first employs rare disease knowledge to synthesize distribution-controllable
reasoning questions. We then utilize the policy model itself to generate high-quality pseudo-labels. This enables a two-stage, intrinsic-to-extrinsic training paradigm: self-supervised RL on the pseudo-labeled synthetic data, followed by supervised RL on the human-annotated real data. MedSSR scales model training efficiently without relying on costly trace
distillation. Extensive experiments on Qwen and Llama demonstrate that our method outperforms existing methods across ten medical
benchmarks, achieving up to +5.93% gain on rare-disease tasks. Our code is available at https://github.com/tdlhl/MedSSR.
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All authors Haolin Li, Shuyang Jiang, Ruipeng Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang
Machine Learning
TempusBench: An Evaluation Framework for Time-Series Forecasting
Denizalp Goktas, Gerardo Riaño-Briceño, Omkar Tekawade et al. (+14 more)
Foundation models have transformed natural
language processing and computer
vision, and a rapidly growing literature on time-series foundation models (TSFMs) seeks to replicate this success in
forecasting. While recent open-source models demonstrate the promise of TSFMs, the field lacks a comprehensive and community-accepted model evaluation framework. We see at least four major issues impeding progress on the development of such a framework. First, existing evaluation frameworks comprise
benchmark forecasting tasks derived from often outdated
datasets (e.g., M3), many of which lack clear metadata and overlap with the corpora used to pre-train TSFMs. Second, these frameworks evaluate models along a narrowly defined set of
benchmark forecasting tasks, such as forecast horizon length or domain, but overlook core statistical properties such as non-stationarity and seasonality. Third, domain-specific models (e.g., XGBoost) are often compared unfairly, as existing frameworks do not enforce a systematic and consistent hyperparameter tuning convention for all models. Fourth, visualization tools for interpreting comparative performance are lacking. To address these issues, we introduce TempusBench, an open-source evaluation framework for TSFMs. TempusBench consists of 1) new
datasets which are not included in existing TSFM pretraining corpora, 2) a set of novel
benchmark tasks that go beyond existing ones, 3) a model evaluation pipeline with a standardized hyperparameter tuning protocol, and 4) a tensorboard-based visualization interface. We provide access to our code on GitHub: https://github.com/Smlcrm/TempusBench and maintain a live leaderboard at https://smlcrm.com/tempusbench.
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All authors Denizalp Goktas, Gerardo Riaño-Briceño, Omkar Tekawade, Alif Abdullah, Md Yasif Jamal, Vinesha Shaik, Arsh Singh, Aryan Nair, Chenkai Shen, Beatriz de Lucio, Alexandra Magnusson, Farhan Mashrur, Ahmed Abdulla, Shawrna Sen, Mahitha Thippireddy, Gregory Schwartz, Amy Greenwald
Machine Learning
Continuous Adversarial Flow Models
Shanchuan Lin, Ceyuan Yang, Zhijie Lin et al. (+2 more)
We propose continuous
adversarial flow models, a type of continuous-time flow model trained with an
adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces samples that are better aligned with the target data distribution. Our method is primarily proposed for post-training existing flow-matching models, although it can also train models from scratch. On the ImageNet 256px generation task, our post-training substantially improves the guidance-free FID of latent-space SiT from 8.26 to 3.63 and of pixel-space JiT from 7.17 to 3.57. It also improves guided generation, reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT. We further evaluate our approach on text-to-image generation, where it achieves improved results on both the GenEval and DPG
benchmarks.
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All authors Shanchuan Lin, Ceyuan Yang, Zhijie Lin, Hao Chen, Haoqi Fan
Machine Learning
Generative Path-Finding Method for Wasserstein Gradient Flow
Chengyu Liu and Xiang Zhou
Wasserstein gradient flows (WGFs) describe the
evolution of probability distributions in Wasserstein space as steepest descent dynamics for a free energy functional. Computing the full path from an arbitrary initial distribution to
equilibrium is challenging, especially in high dimensions. Eulerian methods suffer from the curse of dimensionality, while existing Lagrangian approaches based on particles or generative maps do not naturally improve efficiency through time step tuning. We propose GenWGP, a generative path finding framework for Wasserstein gradient paths. GenWGP learns a generative flow that transports mass from an initial density to an unknown
equilibrium distribution by minimizing a path loss that encodes the full trajectory and its terminal
equilibrium condition. The loss is derived from a
geometric action functional motivated by Dawson Gartner large deviation theory for empirical distributions of interacting
diffusion systems. We formulate both a finite horizon action under physical time parametrization and a reparameterization invariant
geometric action based on Wasserstein arclength. Using normalizing flows, GenWGP computes a
geometric curve toward
equilibrium while enforcing approximately constant intrinsic speed between adjacent network layers, so that discretized distributions remain nearly equidistant in the Wasserstein metric along the path. This avoids delicate time stepping constraints and enables stable training that is largely independent of temporal or
geometric discretization. Experiments on Fokker Planck and aggregation type problems show that GenWGP matches or exceeds high fidelity reference solutions with only about a dozen discretization points while capturing complex dynamics.
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Machine Learning · Cross-listed from cond-mat.str-el
Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Gia-Wei Chern, Yunhao Fan, Sheng Zhang et al. (+1 more)
We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic
spin systems. We generalize the Behler-Parrinello (BP) ML architecture – originally developed for
quantum molecular dynamics – to construct scalable and transferable ML models capable of capturing the intricate dependence of electron-mediated exchange fields on the local
magnetic environment characteristic of itinerant magnets. A central ingredient of this framework is the implementation of
symmetry-aware
magnetic descriptors based on group-theoretical bispectrum formalisms. Leveraging these ML force fields, LLG simulations faithfully reproduce hallmark non-collinear
magnetic orders – such as the $120^\circ$ and tetrahedral states – on the triangular
lattice, and successfully capture the complex
spin textures emerging in the mixed-phase states of a square-
lattice double-exchange model under thermal quench. We further discuss a generalized potential theory that extends the BP formalism to incorporate both conservative and nonconservative electronic torques, thereby enabling ML models to learn nonequilibrium exchange fields from computationally demanding microscopic approaches such as nonequilibrium Green's-function techniques. This extension yields quantitatively accurate predictions of voltage-driven domain-wall motion and establishes a foundation for
quantum-accurate, multiscale modeling of nonequilibrium
spin dynamics and spintronic functionalities.
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All authors Gia-Wei Chern, Yunhao Fan, Sheng Zhang, Puhan Zhang
Machine Learning · Cross-listed from cs.GT
The Price of Ignorance: Information-Free Quotation for Data Retention in Machine Unlearning
Bin Han, Di Feng, Zexin Fang et al. (+2 more)
When users exercise data deletion rights under the General Data Protection Regulation (GDPR) and similar regulations, mobile network operators face a tradeoff: excessive machine unlearning degrades model accuracy and incurs retraining costs, yet existing pricing mechanisms for data retention require the server to know every user's private
privacy and accuracy preferences, which is infeasible under the very regulations that motivate unlearning. We ask: what is the welfare cost of operating without this private information? We design an information-free ascending quotation mechanism where the server broadcasts progressively higher prices and users self-select their data supply, requiring no knowledge of users' parameters. Under complete information, the protocol admits a unique subgame-perfect Nash
equilibrium characterized by single-period selling. We formalize the Price of Ignorance – the welfare gap between optimal personalized pricing (which knows everything) and our information-free quotation (which knows nothing) – and prove a three-regime efficiency ordering. Numerical evaluation across seven mechanisms and 5000 Monte Carlo runs shows that this price is near zero: the information-free mechanism achieves >=99% of the welfare of its information-intensive
benchmarks, while providing noise-robust guarantees and comparable
fairness.
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All authors Bin Han, Di Feng, Zexin Fang, Jie Wang, Hans D. Schotten
Machine Learning · Cross-listed from cs.CL
Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
Jiashu Yao, Heyan Huang, Daiqing Wu et al. (+2 more)
To encourage diverse exploration in reinforcement learning (RL) for large
language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifurcates the policy into normal and high-
entropy modes with a high-
entropy prompt. While sharing model parameters, the two modes undergo collaborative dual-mode
entropy regularization tailored to distinct objectives. Specifically, the normal mode optimizes for task correctness, while the high-
entropy mode incorporates a preference for exploration, and the two modes learn collaboratively. Extensive experiments demonstrate that our approach consistently outperforms established
entropy-guided RL baselines across various model sizes in general and creative tasks. Further analysis reveals that Policy Split facilitates dual-mode exploration, where the high-
entropy mode generates distinct behavioral patterns to the normal mode, providing unique learning signals.
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All authors Jiashu Yao, Heyan Huang, Daiqing Wu, Zeming Liu, Yuhang Guo
Machine Learning
Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers
Miit Daga and Swarna Priya Ramu
Fine-tuning a pretrained classifier leaves some samples reliably learned and others cycling between correct and incorrect. Curriculum learning, data
pruning and
dataset cartography assume that pattern is a property of the sample, untested. We record per-sample correctness at every epoch while fine-tuning ResNet-18 and DeiT-Small on an imbalanced retinal OCT
dataset and CUB-200-2011, matching samples by image identity and holding the split fixed across seeds. Per-sample retention is reproducible: cross-run Spearman correlation of the fitted decay constant is 0.37 to 0.59 over ten seeds. It is architecture-specific: two runs of one backbone agree more than two backbones on identical data (0.45 and 0.59 within against 0.30 between on OCTDL). Loss after five frozen-backbone epochs predicts a different run's decay constant at 0.29 to 0.43. The Ebbinghaus exponential does not survive: monotone decay, the one shape it can represent, is 0.1% to 0.8% of samples, and on traces that do forget mean R-squared is negative in all four configurations. A power law and a free-asymptote variant fail on the same traces: the defect is monotonicity. Across five sampling arms with matched exposure, prioritisation ratios of 2.7x to 28x, and an online variant, none of 48 comparisons against uniform sampling survives Benjamini-Hochberg correction, though three seeds detect only about four accuracy points. A stable, cheap difficulty score does not buy generalisation through sampling. Patient-grouped splitting, the remedy for a leak reaching 76% to 78% of OCT test images, moves that
dataset's headline metrics by less than their run-to-run spread.
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Machine Learning · Cross-listed from math.OC
Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
I. Esra Buyuktahtakin
Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management science (OR/MS), which has long provided methodological foundations for sequential decision making under uncertainty. At the same time, deep learning advances, including feedforward neural networks, recurrent architectures,
transformers, large
language models (LLMs), and deep reinforcement learning, have expanded data-driven modeling for large-scale decisions. This tutorial presents an OR/MS-centered perspective on deep learning for sequential decision making under uncertainty, bridging neural architectures and OR/MS approaches to decision making. Its premise: deep learning complements optimization rather than replacing it. Deep learning brings adaptability and scalable approximation, whereas OR/MS provides the mathematical rigor to represent constraints, recourse, uncertainty, and decision quality. The tutorial reviews key decision making foundations, connects them to the major neural architectures in modern AI, and organizes the field around three central themes: predict-then-optimize and decision-aware learning, learning-based decision generation under constraints for continuous and discrete problems with temporal coupling, and deep reinforcement learning for sequential and combinatorial decision making. Impact spans supply chains, service systems, healthcare and epidemic response, agriculture, energy, environmental sustainability, and autonomous operations. This tutorial frames these developments as part of a shift from predictive AI toward decision-capable AI, highlighting OR/MS's role in shaping the next generation of integrated learning–optimization systems.
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Machine Learning
Attention-Weighted Value Projection for KV-Cache Compression
Samuel Salfati
Rank reduction discards dimensions; quantization keeps them at lower precision. Comparing the two requires a choice of what compression should preserve. For
attention values, we study reconstruction of the
attention output rather than reconstruction of the values alone. With fixed
attention weights, the optimal orthogonal rank-$r$ projection uses the leading eigenvectors of $V^\top\alpha^\top\alpha V$, and its error is exactly the discarded
eigenvalue sum. We extend this objective to calibration
datasets and grouped query
attention, and describe rank allocation under an additive local error budget. We also examine the limits of using local error to predict downstream loss. Historical weight-perturbation experiments favor coefficient rounding over the value projections tested, but do not establish a comparison at equal cache storage. The resulting distinction is practical: the projection objective has an exact solution, while a comparison with cache quantization requires separate activation-level experiments.
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Machine Learning · Cross-listed from cs.CV
Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference
Imanol Miranda, Ander Salaberria, Eneko Agirre et al. (+1 more)
Dual-encoder
Vision-
Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional
benchmarks. We argue that this limitation may stem less from deficient representations than from the standard inference protocol based on global cosine similarity. First, through controlled diagnostic experiments, we show that explicitly enforcing fine-grained region-segment
alignment at inference dramatically improves compositional performance without updating pretrained encoders. We then introduce a lightweight
transformer that learns such
alignments directly from frozen patch and
token embeddings. Comparing against full fine-tuning and prior end-to-end compositional training methods, we find that although these approaches improve in-domain
retrieval, their gains do not consistently transfer under distribution shift. In contrast, learning localized
alignment over frozen representations matches full fine-tuning on in-domain
retrieval while yielding substantial improvements on controlled out-of-domain compositional
benchmarks. These results identify global
embedding matching as a key bottleneck in dual-encoder VLMs and highlight the importance of
alignment mechanisms for robust compositional generalization.
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All authors Imanol Miranda, Ander Salaberria, Eneko Agirre, Gorka Azkune
Machine Learning · Cross-listed from stat.ML
ADD for Multi-Bit Image Watermarking
An Luo and Jie Ding
As generative models enable rapid creation of high-fidelity images, societal concerns about misinformation and authenticity have intensified. A promising remedy is multi-bit image watermarking, which embeds a multi-bit message into an image so that a verifier can later detect whether the image is generated by someone and further identify the source by decoding the embedded message. Existing approaches often fall short in capacity, resilience to common image distortions, and theoretical justification. To address these limitations, we propose ADD (Add, Dot, Decode), a multi-bit image watermarking method with two stages: learning a watermark to be linearly combined with the multi-bit message and added to the image, and decoding through inner products between the watermarked image and the learned watermark. On the standard MS-COCO
benchmark, we demonstrate that for the challenging task of 48-bit watermarking, ADD achieves 100% decoding accuracy, with performance dropping by at most 2% under a wide range of image distortions, substantially smaller than the 14% average drop of state-of-the-art methods. In addition, ADD achieves substantial computational gains, with 2-fold faster
embedding and 7.4-fold faster decoding than the fastest existing method. We further provide a theoretical analysis explaining why the learned watermark and the corresponding decoding rule are effective.
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Machine Learning
CAGenMol: Condition-Aware Diffusion Language Model for Goal-Directed Molecular Generation
Yanting Li, Zhuoyang Jiang, Enyan Dai et al. (+3 more)
Goal-directed molecular generation requires satisfying heterogeneous constraints such as
protein–ligand compatibility and multi-objective drug-like properties, yet existing methods often optimize these constraints in isolation, failing to reconcile conflicting objectives (e.g., affinity vs. safety), and struggle to navigate the non-differentiable chemical space without compromising structural validity. To address these challenges, we propose CAGenMol, a condition-aware discrete
diffusion framework over molecular sequences that formulates molecular design as conditional denoising guided by heterogeneous structural and property signals. By coupling discrete
diffusion with reinforcement learning, the model aligns the generation trajectory with non-differentiable objectives while preserving chemical validity and diversity. The non-autoregressive nature of
diffusion language model further enables iterative refinement of molecular fragments at inference time. Experiments on structure-conditioned, property-conditioned, and dual-conditioned
benchmarks demonstrate consistent improvements over state-of-the-art methods in binding affinity, drug-likeness, and success rate, highlighting the effectiveness of our framework.
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All authors Yanting Li, Zhuoyang Jiang, Enyan Dai, Lei Wang, Wen-Cai Ye, Li Liu